Plotly is a web application for creating and sharing data visualizations. Plotly can work with several programming languages and applications including R, Python, and Microsoft Excel. We're going to concentrate on creating different graphs with Plotly and sharing those graphs.

Installing Plotly

If you want to share your visualizations on https://plot.ly/ you should make an account on their site

library(plotly)
plot_ly(mtcars, x = ~wt, y = ~mpg, type="scatter", height=350, width=780)

Higher cylinder counts have higher weight and generaly low miles per gallon

plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", color = ~factor(cyl),
        height = 350, width = 780)

Coloring the scatterplot with displacement as the continous variable. As shown in the graph, lower disp values tends to have higher miles per gallon and lower weight.

plot_ly(mtcars, x = ~wt, y = ~mpg, mode = "markers", color = ~disp,
        height = 350, width = 780)

Coloring the scatterplot with displacement as the continous variable. As shown in the graph, lower disp values tends to have higher miles per gallon and lower weight.

plot_ly(mtcars, x = ~wt, y = ~mpg, mode = "markers", size = ~hp,
        color = ~factor(cyl), height = 350, width = 780)

set.seed(2016-07-21)
temp <- rnorm(100, mean=30, sd =5)
pressue <- rnorm(100)
dtime <- 1:100
plot_ly(x = temp, y = pressue, z = dtime, type="scatter3d", 
        mode = "markers",color = temp, width = 776, height=350)

The default graph in plotly is a Line Chart. E.g.: Plotting miles travelled over the years in air with airmiles dataset

data("airmiles")
plot_ly(x = time(airmiles), y = airmiles, mode="lines",
        width = 780, height=350)

Stock market data representation is the most common form of multi-line graphs

library(tidyr)
library(dplyr)
data("EuStockMarkets")
stocks <- as.data.frame(EuStockMarkets) %>%
  gather(index, price) %>%
  mutate(time = rep(time(EuStockMarkets), 4))
plot_ly(stocks, x = ~time, y = ~price, color = ~index, type = "scatter", 
        mode = "lines", width = 776, height=280)

Requires one variable to visualize histogram

data("iris")
plot_ly(x = iris$Sepal.Length, type = 'histogram',
        width = 776, height=370)

plot_ly(iris, y = ~Petal.Length, color = ~Species, type = 'box',
        width = 776, height=400)

Plotly takes a matrix as an input and produces a raster image for heatmap

terrain <- matrix(rnorm(100*100), nrow= 100, ncol=100)
plot_ly(z = terrain, type = 'heatmap', width = 776, height=370)

Same as heatmap, only type="surface"

terrain <- matrix(sort(rnorm(100*100)), nrow= 100, ncol=100)
plot_ly(z = terrain, type = 'surface', width = 776, height=370)

Choropleth maps illustrate data across geographic areas by shading regions with different colors. Choropleth maps are easy to make with Plotly though they require more setup compared to other Plotly graphics.

# Create data frame
state_pop <- data.frame(State = state.abb, Pop = as.vector(state.x77[,1]))
# Create hover text
state_pop$hover <- with(state_pop, paste(State, '<br>', "Population:", Pop))
# Make state borders white
borders <- list(color = toRGB("red"))
# Set up some mapping options
map_options <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
plot_ly(z = ~state_pop$Pop, text = ~state_pop$hover, 
        locations = ~state_pop$State, type = 'choropleth', 
        locationmode = 'USA-states', color = state_pop$Pop, 
        colors = 'Blues', marker = list(line = borders), 
        width =776, height=480) %>%
  layout(title = 'US Population in 1975', geo = map_options)